Implicit Neural Representations for Multimodal Longitudinal Image Imputation and Interpolation

• URL: http://arxiv.org/abs/2608.02324v1
• Date: Mon, 03 Aug 2026 14:47:10 GMT
• Title: Implicit Neural Representations for Multimodal Longitudinal Image Imputation and Interpolation
• Abstract summary: Longitudinal multiparametric MRI is central to follow-up imaging in oncology.<n>Real-world clinical data are characterised by missing sequences, heterogeneous acquisition protocols, and varying resolutions across time points.<n>We propose a patient-specific conditional implicit neural representation (INR) that models longitudinal MRI as a continuous function of world coordinates, time, and modality conditioning.
• Score: 0.43554874937871363
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Longitudinal multiparametric MRI is central to follow-up imaging in oncology, yet real-world clinical data are characterised by missing sequences, heterogeneous acquisition protocols, and varying spatial resolutions across time points. We propose a patient-specific conditional implicit neural representation (INR) that models multimodal longitudinal MRI as a continuous function of world coordinates, time, and modality conditioning. The model is trained with stochastic modality dropout to handle incomplete data, and its continuous coordinate-space formulation enables both spatial and temporal interpolation without resampling to a fixed voxel grid. A self-consistency-based confidence estimator is derived from cross-modal reconstruction performance at inference time. We evaluate the framework on longitudinal MRI from paediatric brain tumour patients, demonstrating statistically significant improvements over linear interpolation for T1CE and FLAIR (p < 0.05), with mean MS-SSIM of 0.95 $\pm$ 0.02 for T1CE. Predicted confidence correlates strongly with true reconstruction quality (Pearson r up to 0.996), suggesting reliable deployment potential in heterogeneous clinical settings.

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